Digital twins in the age of AI: How artificial intelligence is shaping the future

How Artificial Intelligence Is Transforming Digital Twin Technology

Sometimes, two technologies help each other soar. It was true of smartphones and mobile internet. And it’s true of digital twins and artificial intelligence.

AI is accelerating the creation of digital twin solutions. It’s also making them more powerful and easier to use. In return, digital twins are offering AI an extraordinary education, providing virtual environments in which its algorithms can learn about the real world like never before.

How AI is elevating digital twins

Building a digital twin can be a slow process. McKinsey suggests that simply designing and developing the digital twin model for a specialized application, such as multimachine production scheduling, can take six months or longer.

But as with so many other coding-intensive projects, Large Language Models (LLMs) now promise to shoulder much of the heavy lifting — and with AI generating code for digital twin solutions, those long lead times should shorten. McKinsey also sees AI helping to produce “universal” models that speed up digital twin creation even further, by providing developers with a generalized digital model they can adapt to their needs.

If you want a digital twin to provide accurate, relevant insights, you need to feed it plenty of real-time data. But the data held by organizations tends to be disparate, diverse, and incomplete. Here, again, AI is ready to lend a helping hand.

AI can gather data from multiple sources, in multiple formats (think maintenance logs, or videos of machines in operation) and present it in a way the digital twin technology can understand. It can even synthesize new data, for example, generating information about a manufacturing defect that’s never occurred before, so the digital twin can spot it when it does.

As you might expect, AI is pitching in with advanced data analysis. When IKEA created digital twins of 37 locations, for example, AI helped the furniture retailer simulate the environmental impact of 6,000 pieces of heating, ventilation, and air conditioning (HVAC) equipment, and ultimately slash the energy consumption of its HVAC central supply system by 30%.

At the same time, AI is making the deep insights generated by digital twins much easier to surface. Engineers at Continental, the automotive parts manufacturer, are already chatting with digital twins of their production systems in natural language, using a specially designed generative AI copilot.

AI’s crucial role in process digital twins

AI is also supercharging digital twins that, rather than modeling a physical object or system, model a business process.

Alongside process data from an organization’s own systems and standardized process knowledge, AI is one of the essential components of a process digital twin. As such, the examples of its use are plentiful — but take the US state’s central finance agency that, with the help of process data, process knowledge and AI, is now able to spot non-compliant POs in real time.

Just as with other digital twin solutions, generative AI can help to:

• Prepare unstructured source data for analysis within your process digital twin. It can also apply reason to this data and make decisions based on a user’s instructions.
• Simplify (and expand) access to your digital twin solution, by acting as a natural language copilot.

How digital twins are elevating AI in return

How are digital twins helping AI to reach new heights? Let’s look at the other side of this mutually reinforcing, technological power couple.

We know that AI is only as good as its training. And digital twins provide incredible training environments.

Imagine you’re developing an autonomous mechanical arm for an ecommerce company’s warehouse. You want the arm to be able to perceive what’s happening in its surroundings and respond appropriately. For example, when the item being picked is delicate and light, the robot needs to understand this, and grip it softly. When the robot is moving between racks, it needs to recognize when a human co-worker is in the way.

For your generative AI to grasp (pun fully intended) how physical objects like its mechanical hand and a heavy box interact, it needs to be trained on additional data about the real world. And this is data that digital twins are primed to provide.

Industry innovators have been quick to realize the opportunity. Foxconn, the world’s largest electronics manufacturer, is using digital twins of its factories to train fleets of autonomous robots ahead of their deployment to the factory floor. The twins are also used to test and optimize the placement of IoT sensors such as intelligent cameras, supporting AI-powered video analytics.

As well as giving AI a better understanding of the real world from the get-go, digital twins can help the technology to rapidly course-correct. When generative AI proposes an action that’s physically impossible, for example, a virtual twin can automatically flag the error, and send the AI back to the drawing board.

Another strong example of digital twins helping to train up AI comes from the UK, where researchers have built a virtual model of the nation’s airspace. The virtual model is being used to develop AI capable of supporting human air-traffic controllers as the skies become ever more crowded.

What about process digital twins? Are they boosting AI too?

Absolutely. In fact, for AI to make sense of a business process and provide accurate, actionable insight, it needs to understand how that process actually works. Across systems, departments, and even organizations. And that’s exactly the context a process digital twin can provide.

It’s similar to the example of the mechanical arm. Just as AI needs some extra information to understand how a gripping mechanism really interacts with a box, it needs some extra information to understand how Procurement (and everybody else) really interacts with a purchase order.

By simply doing what it does naturally — combining data points from multiple sources and formats, and recreating and visualizing the relationships between them — a process digital twin can tell AI everything it needs to know.

Process twins enable organizations to create AI agents which understand the unique way their business runs, and how it could run even better. Whether the AI highlights an opportunity for a radical redesign, or the savvy application of automation to resolve a bottleneck, such process improvements can be tested within the digital twin model. Potential issues fixed, the digital representation of the optimized process can be safely replicated in real life.

With access to a digital twin, AI can also drive processes’ consistency in real time. The US state central finance agency mentioned above, for example, uses insights from its process digital twin not only to automatically flag non-compliant POs, but also to provide AI-driven recommendations on how to correct them.

Digital twins and AI: they should go hand-in-hand

Lennon and McCartney. Peanut butter and jelly. Some things in this world have complementary strengths. Whether you’re more interested in what digital twins can do for your organization or what AI can, you’re sure to see the best results by mastering both.

You can learn more about how we create process digital twins here – or just talk to an expert.